thanks @chris ... thanks a lot ...

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On Sunday, October 16, 2016 at 8:51:56 PM UTC+3:30, Chris Rackauckas wrote:
>
> Take a look at the performance tips
> <http://docs.julialang.org/en/release-0.4/manual/performance-tips/>. The
> first time you run it, the function will compile. Then the compiled
> function is cached. On my computer I did:
>
> a = rand(1000,1000)
> y=similar(a)
> @time a*a
> @time a*a
> @time A_mul_B!(y,a,a)
> @time A_mul_B!(y,a,a)
>
> Which gives output:
>
>
> 0.435561 seconds (367.13 k allocations: 20.108 MB, 1.58% gc time)
> 0.019922 seconds (7 allocations: 7.630 MB)
>
> 0.027144 seconds (53 allocations: 2.875 KB)
> 0.016211 seconds (4 allocations: 160 bytes)
>
> Notice how after compiling, the allocations and the timings go way down.
> For a more in-depth look at how Julia is looking to get the speed (and how
> to make the most of it), take a look at this blog post
> <http://www.stochasticlifestyle.com/7-julia-gotchas-handle/>. Julia is a
> little bit more complex than MATLAB, but the payoffs can be huge once you
> take the time to understand it. Happy Julia-ing!
>
>
> On Sunday, October 16, 2016 at 9:45:00 AM UTC-7, majid.z...@gmail.com
> wrote:
>>
>> i have run the same matrix multiplication in both matlab and julia but
>> matlab in much faster that julia, i have used both A_mul_B! and *()
>> functions
>> my codes are :
>> in matlab :
>> tic
>> a = rand(1000,1000)
>> a*a
>> toc
>> the output is : Elapsed time is 0.193979 seconds
>>
>> in Julia :
>> a = rand(1000,1000)
>> y=similar(a)
>> @time a*a
>> @time A_mul_B!(y,a,a)
>>
>> the output is:
>> 1.575159 seconds
>> 1.497884 seconds
>> Majid
>>
>